Neural network based systems are known, however, known systems typically are not capable of analyzing a large collection of complex data. Rather, neural network based systems typically operate on subsets of the complex data. For example, and to detect abnormalities in image data, known neural network systems typically only analyze subsets of an entire data set, which reduces the effectiveness of the analysis.
One particular application in which this limitation of known neural network based systems has a significant adverse consequence is in mammography screening. Particularly, breast cancer is now estimated to strike one in eight adult American women, and many national institutions are promoting large-scale breast cancer screening programs. Even with the above described limitations, computer-aided diagnosis techniques have been applied to mammography screening programs and such techniques may offer substantial benefits in terms of cost reduction and increased effectiveness of the screening process. The use of computers to directly prescreen mammograms may eventually permit a substantial reduction in the number of studies that must be viewed by a radiologist. Of course, to achieve such a reduction, computers must be able to directly interpret digitized images and the process must be fully automated.
With respect to digitized images, mammograms show only an estimated 3% of their actual information content. Improvements in the visibility of mammographic information content will probably improve detection of small tumors. It is unlikely, however, that state-of-the-art screen-film radiography alone can be improved to display more information.
Wavelet transformation, a known image enhancement technique, has been used successfully to enhance the visibility of image information content in mammograms, including both masses and microcalcifications. Wavelet image representations also permit high magnitudes of data compression without loss of important image features.
To interpret the digitized images, several rule-based systems use thresholding, subtraction, or both. These techniques have been hampered by high false-positive detection rates. Artificial neural networks (ANN) are an alternative to traditional rule-based (symbolic) methods for computer-aided detection of mammographic lesions. ANNs learn the significance of image features based upon example training images. In general, ANNs are adept at pattern recognition problems.
As explained above, however, most all known ANN systems perform direct digitized data analysis of mammograms using only small regions of interest (ROI) selected from an entire image. Other ANN systems extract features, either qualitative or quantitative, for network training, or have incorporated ANNs into other computer-aided diagnosis schemes to improve lesion detection.
It would be desirable to provide a fully automated system for detecting anomalies in complex data, including for analyzing entire images to identify lesions. It also would be desirable to provide such a system which does not generate a high false-positive detection rate.